<<<<<<< HEAD VisualPanorama - Survey Results

Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations894
Missing cells15,342
Missing cells (%)46.4%
Duplicate rows3
Duplicate rows (%)0.3%
Total size in memory1.5 MiB
Average record size in memory1.7 KiB

Variable types

Categorical32
Boolean1
Text3
Unsupported1

Alerts

felt_sympathetic_to_victims has constant value "3.Undecided" Constant
impact_on_own_life has constant value "3.Undecided" Constant
impact_on_society has constant value "3.Undecided" Constant
Dataset has 3 (0.3%) duplicate rowsDuplicates
experienced_activity_feeling is highly overall correlated with felt_part_of_activity and 2 other fieldsHigh correlation
felt_part_of_activity is highly overall correlated with experienced_activity_feeling and 3 other fieldsHigh correlation
gender_identity is highly overall correlated with personal_connection_detailsHigh correlation
involvement_over_irrelevant_thoughts is highly overall correlated with experienced_activity_feeling and 4 other fieldsHigh correlation
knowledge_bergen_belsen is highly overall correlated with knowledge_other_persecuted_groups and 1 other fieldsHigh correlation
knowledge_other_persecuted_groups is highly overall correlated with knowledge_bergen_belsen and 2 other fieldsHigh correlation
knowledge_persecuted_jews is highly overall correlated with knowledge_other_persecuted_groups and 2 other fieldsHigh correlation
knowledge_ww2 is highly overall correlated with knowledge_bergen_belsen and 2 other fieldsHigh correlation
left_weak_impression is highly overall correlated with thought_innovative and 1 other fieldsHigh correlation
lost_track_of_time is highly overall correlated with experienced_activity_feeling and 5 other fieldsHigh correlation
nationality is highly overall correlated with personal_connection_nazi_historyHigh correlation
personal_connection_details is highly overall correlated with gender_identity and 3 other fieldsHigh correlation
personal_connection_nazi_history is highly overall correlated with nationality and 1 other fieldsHigh correlation
plan_to_learn_more is highly overall correlated with involvement_over_irrelevant_thoughts and 3 other fieldsHigh correlation
thought_innovative is highly overall correlated with left_weak_impression and 2 other fieldsHigh correlation
understood_camp_appearance is highly overall correlated with understood_camp_function and 1 other fieldsHigh correlation
understood_camp_function is highly overall correlated with understood_camp_appearance and 1 other fieldsHigh correlation
understood_life_in_camp is highly overall correlated with understood_camp_appearance and 1 other fieldsHigh correlation
visit_purpose is highly overall correlated with felt_part_of_activityHigh correlation
want_to_share_learning is highly overall correlated with involvement_over_irrelevant_thoughts and 3 other fieldsHigh correlation
was_boring is highly overall correlated with left_weak_impression and 1 other fieldsHigh correlation
was_interesting is highly overall correlated with plan_to_learn_more and 2 other fieldsHigh correlation
nationality is highly imbalanced (57.1%) Imbalance
political_identity is highly imbalanced (68.7%) Imbalance
knowledge_ww2 is highly imbalanced (66.3%) Imbalance
knowledge_bergen_belsen is highly imbalanced (67.5%) Imbalance
knowledge_persecuted_jews is highly imbalanced (67.1%) Imbalance
knowledge_other_persecuted_groups is highly imbalanced (69.1%) Imbalance
videogame_frequency is highly imbalanced (81.1%) Imbalance
felt_part_of_activity is highly imbalanced (94.2%) Imbalance
involvement_over_irrelevant_thoughts is highly imbalanced (95.4%) Imbalance
experienced_activity_feeling is highly imbalanced (94.8%) Imbalance
lost_track_of_time is highly imbalanced (96.9%) Imbalance
was_interesting is highly imbalanced (95.3%) Imbalance
left_weak_impression is highly imbalanced (96.5%) Imbalance
was_boring is highly imbalanced (94.3%) Imbalance
thought_innovative is highly imbalanced (96.5%) Imbalance
understood_camp_appearance is highly imbalanced (94.3%) Imbalance
understood_life_in_camp is highly imbalanced (95.2%) Imbalance
understood_camp_function is highly imbalanced (94.3%) Imbalance
want_to_share_learning is highly imbalanced (90.0%) Imbalance
plan_to_learn_more is highly imbalanced (90.8%) Imbalance
nationality has 749 (83.8%) missing values Missing
gender_identity has 727 (81.3%) missing values Missing
age has 725 (81.1%) missing values Missing
education_level has 742 (83.0%) missing values Missing
visit_type has 735 (82.2%) missing values Missing
visit_purpose has 745 (83.3%) missing values Missing
religious has 739 (82.7%) missing values Missing
political_identity has 148 (16.6%) missing values Missing
visited_memorial_before has 746 (83.4%) missing values Missing
personal_connection_nazi_history has 742 (83.0%) missing values Missing
personal_connection_details has 865 (96.8%) missing values Missing
knowledge_ww2 has 148 (16.6%) missing values Missing
knowledge_bergen_belsen has 149 (16.7%) missing values Missing
knowledge_persecuted_jews has 150 (16.8%) missing values Missing
knowledge_other_persecuted_groups has 150 (16.8%) missing values Missing
known_persecuted_groups_open has 827 (92.5%) missing values Missing
technologies_used has 816 (91.3%) missing values Missing
felt_part_of_activity has 172 (19.2%) missing values Missing
involvement_over_irrelevant_thoughts has 173 (19.4%) missing values Missing
experienced_activity_feeling has 172 (19.2%) missing values Missing
lost_track_of_time has 172 (19.2%) missing values Missing
was_interesting has 174 (19.5%) missing values Missing
left_weak_impression has 174 (19.5%) missing values Missing
was_boring has 174 (19.5%) missing values Missing
thought_innovative has 174 (19.5%) missing values Missing
understood_camp_appearance has 173 (19.4%) missing values Missing
understood_life_in_camp has 173 (19.4%) missing values Missing
understood_camp_function has 173 (19.4%) missing values Missing
felt_sympathetic_to_victims has 175 (19.6%) missing values Missing
impact_on_own_life has 175 (19.6%) missing values Missing
impact_on_society has 175 (19.6%) missing values Missing
impact_society_details has 894 (100.0%) missing values Missing
want_to_share_learning has 169 (18.9%) missing values Missing
plan_to_learn_more has 169 (18.9%) missing values Missing
additional_feedback has 891 (99.7%) missing values Missing
email has 887 (99.2%) missing values Missing
impact_society_details is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-05-15 15:49:52.938677
Analysis finished2025-05-15 15:49:59.628225
Duration6.69 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

nationality
Categorical

High correlation  Imbalance  Missing 

Distinct26
Distinct (%)17.9%
Missing749
Missing (%)83.8%
Memory size55.9 KiB
Germany
106 
United Kingdom
 
5
Netherlands
 
4
Turkey
 
3
Spam
 
3
Other values (21)
24 

Length

Max length14
Median length7
Mean length7.2758621
Min length4

Characters and Unicode

Total characters1,055
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)13.1%

Sample

1st rowGermany
2nd rowPoland
3rd rowSpam
4th rowIreland
5th rowGermany

Common Values

ValueCountFrequency (%)
Germany 106
 
11.9%
United Kingdom 5
 
0.6%
Netherlands 4
 
0.4%
Turkey 3
 
0.3%
Spam 3
 
0.3%
Poland 3
 
0.3%
France 2
 
0.2%
Iran 1
 
0.1%
Mexico 1
 
0.1%
Ireland 1
 
0.1%
Other values (16) 16
 
1.8%
(Missing) 749
83.8%

Length

2025-05-15T17:49:59.753332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
germany 106
69.7%
united 6
 
3.9%
kingdom 5
 
3.3%
netherlands 4
 
2.6%
turkey 3
 
2.0%
spam 3
 
2.0%
poland 3
 
2.0%
france 2
 
1.3%
iran 1
 
0.7%
mexico 1
 
0.7%
Other values (18) 18
 
11.8%

Most occurring characters

ValueCountFrequency (%)
e 140
13.3%
n 137
13.0%
a 135
12.8%
r 126
11.9%
m 117
11.1%
y 109
10.3%
G 107
10.1%
i 20
 
1.9%
d 20
 
1.9%
t 14
 
1.3%
Other values (30) 130
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1055
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 140
13.3%
n 137
13.0%
a 135
12.8%
r 126
11.9%
m 117
11.1%
y 109
10.3%
G 107
10.1%
i 20
 
1.9%
d 20
 
1.9%
t 14
 
1.3%
Other values (30) 130
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1055
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 140
13.3%
n 137
13.0%
a 135
12.8%
r 126
11.9%
m 117
11.1%
y 109
10.3%
G 107
10.1%
i 20
 
1.9%
d 20
 
1.9%
t 14
 
1.3%
Other values (30) 130
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1055
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 140
13.3%
n 137
13.0%
a 135
12.8%
r 126
11.9%
m 117
11.1%
y 109
10.3%
G 107
10.1%
i 20
 
1.9%
d 20
 
1.9%
t 14
 
1.3%
Other values (30) 130
12.3%

gender_identity
Categorical

High correlation  Missing 

Distinct5
Distinct (%)3.0%
Missing727
Missing (%)81.3%
Memory size55.6 KiB
Female
79 
Male
77 
Spam
 
5
Prefer not to say
 
4
Other
 
2

Length

Max length17
Median length6
Mean length5.2694611
Min length4

Characters and Unicode

Total characters880
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowSpam
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 79
 
8.8%
Male 77
 
8.6%
Spam 5
 
0.6%
Prefer not to say 4
 
0.4%
Other 2
 
0.2%
(Missing) 727
81.3%

Length

2025-05-15T17:49:59.920173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:00.083058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 79
44.1%
male 77
43.0%
spam 5
 
2.8%
prefer 4
 
2.2%
not 4
 
2.2%
to 4
 
2.2%
say 4
 
2.2%
other 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 245
27.8%
a 165
18.8%
l 156
17.7%
m 84
 
9.5%
F 79
 
9.0%
M 77
 
8.8%
12
 
1.4%
t 10
 
1.1%
r 10
 
1.1%
o 8
 
0.9%
Other values (9) 34
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 245
27.8%
a 165
18.8%
l 156
17.7%
m 84
 
9.5%
F 79
 
9.0%
M 77
 
8.8%
12
 
1.4%
t 10
 
1.1%
r 10
 
1.1%
o 8
 
0.9%
Other values (9) 34
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 245
27.8%
a 165
18.8%
l 156
17.7%
m 84
 
9.5%
F 79
 
9.0%
M 77
 
8.8%
12
 
1.4%
t 10
 
1.1%
r 10
 
1.1%
o 8
 
0.9%
Other values (9) 34
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 245
27.8%
a 165
18.8%
l 156
17.7%
m 84
 
9.5%
F 79
 
9.0%
M 77
 
8.8%
12
 
1.4%
t 10
 
1.1%
r 10
 
1.1%
o 8
 
0.9%
Other values (9) 34
 
3.9%

age
Categorical

Missing 

Distinct10
Distinct (%)5.9%
Missing725
Missing (%)81.1%
Memory size59.9 KiB
16–18
29 
25–34
24 
45–54
24 
55–64
22 
35–44
20 
Other values (5)
50 

Length

Max length8
Median length5
Mean length5.1360947
Min length3

Characters and Unicode

Total characters868
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16–18
2nd row85+
3rd row16–18
4th row85+
5th row18–24

Common Values

ValueCountFrequency (%)
16–18 29
 
3.2%
25–34 24
 
2.7%
45–54 24
 
2.7%
55–64 22
 
2.5%
35–44 20
 
2.2%
65–74 18
 
2.0%
Under 16 13
 
1.5%
18–24 9
 
1.0%
85+ 8
 
0.9%
75–84 2
 
0.2%
(Missing) 725
81.1%

Length

2025-05-15T17:50:00.232608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:00.355326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
16–18 29
15.9%
25–34 24
13.2%
45–54 24
13.2%
55–64 22
12.1%
35–44 20
11.0%
65–74 18
9.9%
under 13
7.1%
16 13
7.1%
18–24 9
 
4.9%
85 8
 
4.4%

Most occurring characters

ValueCountFrequency (%)
5 164
18.9%
4 163
18.8%
148
17.1%
6 82
9.4%
1 80
9.2%
8 48
 
5.5%
3 44
 
5.1%
2 33
 
3.8%
7 20
 
2.3%
U 13
 
1.5%
Other values (6) 73
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 868
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 164
18.9%
4 163
18.8%
148
17.1%
6 82
9.4%
1 80
9.2%
8 48
 
5.5%
3 44
 
5.1%
2 33
 
3.8%
7 20
 
2.3%
U 13
 
1.5%
Other values (6) 73
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 868
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 164
18.9%
4 163
18.8%
148
17.1%
6 82
9.4%
1 80
9.2%
8 48
 
5.5%
3 44
 
5.1%
2 33
 
3.8%
7 20
 
2.3%
U 13
 
1.5%
Other values (6) 73
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 868
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 164
18.9%
4 163
18.8%
148
17.1%
6 82
9.4%
1 80
9.2%
8 48
 
5.5%
3 44
 
5.1%
2 33
 
3.8%
7 20
 
2.3%
U 13
 
1.5%
Other values (6) 73
8.4%

education_level
Categorical

Missing 

Distinct6
Distinct (%)3.9%
Missing742
Missing (%)83.0%
Memory size57.9 KiB
Vocational training or apprenticeship
44 
High school
34 
Bachelor's degree
23 
Secondary school
23 
Master's degree
22 

Length

Max length37
Median length17
Mean length20.690789
Min length9

Characters and Unicode

Total characters3,145
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecondary school
2nd rowSecondary school
3rd rowDoctorate
4th rowSecondary school
5th rowBachelor's degree

Common Values

ValueCountFrequency (%)
Vocational training or apprenticeship 44
 
4.9%
High school 34
 
3.8%
Bachelor's degree 23
 
2.6%
Secondary school 23
 
2.6%
Master's degree 22
 
2.5%
Doctorate 6
 
0.7%
(Missing) 742
83.0%

Length

2025-05-15T17:50:00.541442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:00.644919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
school 57
14.8%
degree 45
11.7%
vocational 44
11.4%
or 44
11.4%
training 44
11.4%
apprenticeship 44
11.4%
high 34
8.8%
bachelor's 23
6.0%
secondary 23
6.0%
master's 22
 
5.7%

Most occurring characters

ValueCountFrequency (%)
o 304
 
9.7%
e 297
 
9.4%
i 254
 
8.1%
r 251
 
8.0%
a 250
 
7.9%
234
 
7.4%
n 199
 
6.3%
c 197
 
6.3%
s 168
 
5.3%
t 166
 
5.3%
Other values (13) 825
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 304
 
9.7%
e 297
 
9.4%
i 254
 
8.1%
r 251
 
8.0%
a 250
 
7.9%
234
 
7.4%
n 199
 
6.3%
c 197
 
6.3%
s 168
 
5.3%
t 166
 
5.3%
Other values (13) 825
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 304
 
9.7%
e 297
 
9.4%
i 254
 
8.1%
r 251
 
8.0%
a 250
 
7.9%
234
 
7.4%
n 199
 
6.3%
c 197
 
6.3%
s 168
 
5.3%
t 166
 
5.3%
Other values (13) 825
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 304
 
9.7%
e 297
 
9.4%
i 254
 
8.1%
r 251
 
8.0%
a 250
 
7.9%
234
 
7.4%
n 199
 
6.3%
c 197
 
6.3%
s 168
 
5.3%
t 166
 
5.3%
Other values (13) 825
26.2%

visit_type
Categorical

Missing 

Distinct7
Distinct (%)4.4%
Missing735
Missing (%)82.2%
Memory size58.1 KiB
As an adult with other adults
60 
As a student with my group
39 
Alone
29 
As an adult with children
17 
Other
10 
Other values (2)
 
4

Length

Max length29
Median length26
Mean length21.446541
Min length4

Characters and Unicode

Total characters3,410
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowAs a student with my group
2nd rowAlone
3rd rowAs a student with my group
4th rowAs an adult with other adults
5th rowAs an adult with children

Common Values

ValueCountFrequency (%)
As an adult with other adults 60
 
6.7%
As a student with my group 39
 
4.4%
Alone 29
 
3.2%
As an adult with children 17
 
1.9%
Other 10
 
1.1%
Spam 3
 
0.3%
As a kid with my parents 1
 
0.1%
(Missing) 735
82.2%

Length

2025-05-15T17:50:00.851828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:01.036126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
as 117
16.1%
with 117
16.1%
an 77
10.6%
adult 77
10.6%
other 70
9.6%
adults 60
8.3%
a 40
 
5.5%
my 40
 
5.5%
student 39
 
5.4%
group 39
 
5.4%
Other values (5) 51
7.0%

Most occurring characters

ValueCountFrequency (%)
568
16.7%
t 403
11.8%
a 258
 
7.6%
s 217
 
6.4%
u 215
 
6.3%
h 204
 
6.0%
d 194
 
5.7%
l 183
 
5.4%
n 163
 
4.8%
e 156
 
4.6%
Other values (13) 849
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
568
16.7%
t 403
11.8%
a 258
 
7.6%
s 217
 
6.4%
u 215
 
6.3%
h 204
 
6.0%
d 194
 
5.7%
l 183
 
5.4%
n 163
 
4.8%
e 156
 
4.6%
Other values (13) 849
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
568
16.7%
t 403
11.8%
a 258
 
7.6%
s 217
 
6.4%
u 215
 
6.3%
h 204
 
6.0%
d 194
 
5.7%
l 183
 
5.4%
n 163
 
4.8%
e 156
 
4.6%
Other values (13) 849
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
568
16.7%
t 403
11.8%
a 258
 
7.6%
s 217
 
6.4%
u 215
 
6.3%
h 204
 
6.0%
d 194
 
5.7%
l 183
 
5.4%
n 163
 
4.8%
e 156
 
4.6%
Other values (13) 849
24.9%

visit_purpose
Categorical

High correlation  Missing 

Distinct13
Distinct (%)8.7%
Missing745
Missing (%)83.3%
Memory size59.4 KiB
To learn more about the history
86 
To learn more about the history__For commemoration
18 
Other
15 
For commemoration
14 
To learn more about the history__For commemoration__For research
 
4
Other values (8)
12 

Length

Max length71
Median length31
Mean length30.899329
Min length5

Characters and Unicode

Total characters4,604
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)3.4%

Sample

1st rowTo learn more about the history
2nd rowOther
3rd rowTo learn more about the history
4th rowTo learn more about the history
5th rowTo learn more about the history

Common Values

ValueCountFrequency (%)
To learn more about the history 86
 
9.6%
To learn more about the history__For commemoration 18
 
2.0%
Other 15
 
1.7%
For commemoration 14
 
1.6%
To learn more about the history__For commemoration__For research 4
 
0.4%
To learn more about the history__For research 3
 
0.3%
To learn more about the history__Other 2
 
0.2%
For research 2
 
0.2%
For commemoration_For research 1
 
0.1%
To learn more about the history__For commemoration__For research__Other 1
 
0.1%
Other values (3) 3
 
0.3%
(Missing) 745
83.3%

Length

2025-05-15T17:50:01.261266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to 116
14.9%
learn 116
14.9%
more 116
14.9%
about 116
14.9%
the 116
14.9%
history 86
11.0%
commemoration 32
 
4.1%
history__for 28
 
3.6%
for 18
 
2.3%
other 15
 
1.9%
Other values (6) 22
 
2.8%

Most occurring characters

ValueCountFrequency (%)
o 636
13.8%
632
13.7%
r 485
10.5%
e 433
9.4%
t 409
8.9%
a 284
 
6.2%
h 265
 
5.8%
m 236
 
5.1%
i 156
 
3.4%
n 156
 
3.4%
Other values (10) 912
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 636
13.8%
632
13.7%
r 485
10.5%
e 433
9.4%
t 409
8.9%
a 284
 
6.2%
h 265
 
5.8%
m 236
 
5.1%
i 156
 
3.4%
n 156
 
3.4%
Other values (10) 912
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 636
13.8%
632
13.7%
r 485
10.5%
e 433
9.4%
t 409
8.9%
a 284
 
6.2%
h 265
 
5.8%
m 236
 
5.1%
i 156
 
3.4%
n 156
 
3.4%
Other values (10) 912
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 636
13.8%
632
13.7%
r 485
10.5%
e 433
9.4%
t 409
8.9%
a 284
 
6.2%
h 265
 
5.8%
m 236
 
5.1%
i 156
 
3.4%
n 156
 
3.4%
Other values (10) 912
19.8%

religious
Categorical

Missing 

Distinct3
Distinct (%)1.9%
Missing739
Missing (%)82.7%
Memory size55.4 KiB
Yes
68 
No
64 
I don't know
23 

Length

Max length12
Median length3
Mean length3.9225806
Min length2

Characters and Unicode

Total characters608
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowI don't know
4th rowI don't know
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 68
 
7.6%
No 64
 
7.2%
I don't know 23
 
2.6%
(Missing) 739
82.7%

Length

2025-05-15T17:50:01.389880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:01.472079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes 68
33.8%
no 64
31.8%
i 23
 
11.4%
don't 23
 
11.4%
know 23
 
11.4%

Most occurring characters

ValueCountFrequency (%)
o 110
18.1%
Y 68
11.2%
e 68
11.2%
s 68
11.2%
N 64
10.5%
46
7.6%
n 46
7.6%
I 23
 
3.8%
d 23
 
3.8%
' 23
 
3.8%
Other values (3) 69
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 110
18.1%
Y 68
11.2%
e 68
11.2%
s 68
11.2%
N 64
10.5%
46
7.6%
n 46
7.6%
I 23
 
3.8%
d 23
 
3.8%
' 23
 
3.8%
Other values (3) 69
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 110
18.1%
Y 68
11.2%
e 68
11.2%
s 68
11.2%
N 64
10.5%
46
7.6%
n 46
7.6%
I 23
 
3.8%
d 23
 
3.8%
' 23
 
3.8%
Other values (3) 69
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 110
18.1%
Y 68
11.2%
e 68
11.2%
s 68
11.2%
N 64
10.5%
46
7.6%
n 46
7.6%
I 23
 
3.8%
d 23
 
3.8%
' 23
 
3.8%
Other values (3) 69
11.3%

political_identity
Categorical

Imbalance  Missing 

Distinct5
Distinct (%)0.7%
Missing148
Missing (%)16.6%
Memory size57.1 KiB
3.Centre
659 
2.Left of centre
 
44
5.Far right
 
17
1.Far left
 
15
4.Right of centre
 
11

Length

Max length17
Median length8
Mean length8.7131367
Min length8

Characters and Unicode

Total characters6,500
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Centre
2nd row3.Centre
3rd row3.Centre
4th row3.Centre
5th row3.Centre

Common Values

ValueCountFrequency (%)
3.Centre 659
73.7%
2.Left of centre 44
 
4.9%
5.Far right 17
 
1.9%
1.Far left 15
 
1.7%
4.Right of centre 11
 
1.2%
(Missing) 148
 
16.6%

Length

2025-05-15T17:50:01.655480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:01.758152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.centre 659
74.2%
of 55
 
6.2%
centre 55
 
6.2%
2.left 44
 
5.0%
5.far 17
 
1.9%
right 17
 
1.9%
1.far 15
 
1.7%
left 15
 
1.7%
4.right 11
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 1487
22.9%
t 801
12.3%
r 763
11.7%
. 746
11.5%
n 714
11.0%
C 659
10.1%
3 659
10.1%
142
 
2.2%
f 114
 
1.8%
c 55
 
0.8%
Other values (13) 360
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1487
22.9%
t 801
12.3%
r 763
11.7%
. 746
11.5%
n 714
11.0%
C 659
10.1%
3 659
10.1%
142
 
2.2%
f 114
 
1.8%
c 55
 
0.8%
Other values (13) 360
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1487
22.9%
t 801
12.3%
r 763
11.7%
. 746
11.5%
n 714
11.0%
C 659
10.1%
3 659
10.1%
142
 
2.2%
f 114
 
1.8%
c 55
 
0.8%
Other values (13) 360
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1487
22.9%
t 801
12.3%
r 763
11.7%
. 746
11.5%
n 714
11.0%
C 659
10.1%
3 659
10.1%
142
 
2.2%
f 114
 
1.8%
c 55
 
0.8%
Other values (13) 360
 
5.5%

visited_memorial_before
Categorical

Missing 

Distinct3
Distinct (%)2.0%
Missing746
Missing (%)83.4%
Memory size56.9 KiB
No
55 
Yes, more than once before
49 
Yes, once before
44 

Length

Max length26
Median length16
Mean length14.108108
Min length2

Characters and Unicode

Total characters2,088
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes, once before
2nd rowYes, more than once before
3rd rowNo
4th rowYes, more than once before
5th rowYes, once before

Common Values

ValueCountFrequency (%)
No 55
 
6.2%
Yes, more than once before 49
 
5.5%
Yes, once before 44
 
4.9%
(Missing) 746
83.4%

Length

2025-05-15T17:50:02.088567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:02.196503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes 93
21.5%
before 93
21.5%
once 93
21.5%
no 55
12.7%
than 49
11.3%
more 49
11.3%

Most occurring characters

ValueCountFrequency (%)
e 421
20.2%
o 290
13.9%
284
13.6%
r 142
 
6.8%
n 142
 
6.8%
s 93
 
4.5%
, 93
 
4.5%
Y 93
 
4.5%
f 93
 
4.5%
b 93
 
4.5%
Other values (6) 344
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 421
20.2%
o 290
13.9%
284
13.6%
r 142
 
6.8%
n 142
 
6.8%
s 93
 
4.5%
, 93
 
4.5%
Y 93
 
4.5%
f 93
 
4.5%
b 93
 
4.5%
Other values (6) 344
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 421
20.2%
o 290
13.9%
284
13.6%
r 142
 
6.8%
n 142
 
6.8%
s 93
 
4.5%
, 93
 
4.5%
Y 93
 
4.5%
f 93
 
4.5%
b 93
 
4.5%
Other values (6) 344
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 421
20.2%
o 290
13.9%
284
13.6%
r 142
 
6.8%
n 142
 
6.8%
s 93
 
4.5%
, 93
 
4.5%
Y 93
 
4.5%
f 93
 
4.5%
b 93
 
4.5%
Other values (6) 344
16.5%

personal_connection_nazi_history
Boolean

High correlation  Missing 

Distinct2
Distinct (%)1.3%
Missing742
Missing (%)83.0%
Memory size8.7 KiB
False
120 
True
 
32
(Missing)
742 
ValueCountFrequency (%)
False 120
 
13.4%
True 32
 
3.6%
(Missing) 742
83.0%
2025-05-15T17:50:02.287953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

personal_connection_details
Categorical

High correlation  Missing 

Distinct12
Distinct (%)41.4%
Missing865
Missing (%)96.8%
Memory size56.4 KiB
Relatives were persecuted
Prefer not to say
Other
Unspecified
Relatives were perpetrators
Other values (7)
10 

Length

Max length59
Median length35
Mean length24.275862
Min length4

Characters and Unicode

Total characters704
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)13.8%

Sample

1st rowPrefer not to say
2nd rowI am a survivor of Nazi persecution
3rd rowAncestors were displaced
4th rowRelatives were in the resistance
5th rowRelatives were perpetrators

Common Values

ValueCountFrequency (%)
Relatives were persecuted 5
 
0.6%
Prefer not to say 4
 
0.4%
Other 4
 
0.4%
Unspecified 3
 
0.3%
Relatives were perpetrators 3
 
0.3%
Relatives were part of the allied forces 2
 
0.2%
I am a survivor of Nazi persecution 2
 
0.2%
Relatives were persecuted__Relatives were perpetrators 2
 
0.2%
Relatives were in the resistance 1
 
0.1%
Ancestors were displaced 1
 
0.1%
Other values (2) 2
 
0.2%
(Missing) 865
96.8%

Length

2025-05-15T17:50:02.397347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
were 18
17.8%
relatives 14
 
13.9%
persecuted 5
 
5.0%
perpetrators 5
 
5.0%
prefer 4
 
4.0%
to 4
 
4.0%
not 4
 
4.0%
say 4
 
4.0%
other 4
 
4.0%
the 4
 
4.0%
Other values (17) 35
34.7%

Most occurring characters

ValueCountFrequency (%)
e 140
19.9%
72
10.2%
r 66
 
9.4%
t 58
 
8.2%
s 50
 
7.1%
a 40
 
5.7%
i 36
 
5.1%
p 27
 
3.8%
o 24
 
3.4%
l 22
 
3.1%
Other values (20) 169
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 140
19.9%
72
10.2%
r 66
 
9.4%
t 58
 
8.2%
s 50
 
7.1%
a 40
 
5.7%
i 36
 
5.1%
p 27
 
3.8%
o 24
 
3.4%
l 22
 
3.1%
Other values (20) 169
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 140
19.9%
72
10.2%
r 66
 
9.4%
t 58
 
8.2%
s 50
 
7.1%
a 40
 
5.7%
i 36
 
5.1%
p 27
 
3.8%
o 24
 
3.4%
l 22
 
3.1%
Other values (20) 169
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 140
19.9%
72
10.2%
r 66
 
9.4%
t 58
 
8.2%
s 50
 
7.1%
a 40
 
5.7%
i 36
 
5.1%
p 27
 
3.8%
o 24
 
3.4%
l 22
 
3.1%
Other values (20) 169
24.0%

knowledge_ww2
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.7%
Missing148
Missing (%)16.6%
Memory size58.7 KiB
3.Somewhat
638 
4.More than average
70 
5.Very much
 
29
2.Very little
 
6
1.Not at all
 
3

Length

Max length19
Median length10
Mean length10.91555
Min length10

Characters and Unicode

Total characters8,143
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Somewhat
2nd row3.Somewhat
3rd row3.Somewhat
4th row3.Somewhat
5th row3.Somewhat

Common Values

ValueCountFrequency (%)
3.Somewhat 638
71.4%
4.More than average 70
 
7.8%
5.Very much 29
 
3.2%
2.Very little 6
 
0.7%
1.Not at all 3
 
0.3%
(Missing) 148
 
16.6%

Length

2025-05-15T17:50:02.518530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:02.611304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.somewhat 638
68.8%
4.more 70
 
7.6%
than 70
 
7.6%
average 70
 
7.6%
5.very 29
 
3.1%
much 29
 
3.1%
2.very 6
 
0.6%
little 6
 
0.6%
1.not 3
 
0.3%
at 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 889
10.9%
a 854
10.5%
. 746
9.2%
h 737
9.1%
t 726
8.9%
o 711
8.7%
m 667
8.2%
S 638
7.8%
3 638
7.8%
w 638
7.8%
Other values (17) 899
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8143
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 889
10.9%
a 854
10.5%
. 746
9.2%
h 737
9.1%
t 726
8.9%
o 711
8.7%
m 667
8.2%
S 638
7.8%
3 638
7.8%
w 638
7.8%
Other values (17) 899
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8143
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 889
10.9%
a 854
10.5%
. 746
9.2%
h 737
9.1%
t 726
8.9%
o 711
8.7%
m 667
8.2%
S 638
7.8%
3 638
7.8%
w 638
7.8%
Other values (17) 899
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8143
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 889
10.9%
a 854
10.5%
. 746
9.2%
h 737
9.1%
t 726
8.9%
o 711
8.7%
m 667
8.2%
S 638
7.8%
3 638
7.8%
w 638
7.8%
Other values (17) 899
11.0%

knowledge_bergen_belsen
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.7%
Missing149
Missing (%)16.7%
Memory size58.5 KiB
3.Somewhat
654 
4.More than average
 
35
2.Very little
 
34
5.Very much
 
12
1.Not at all
 
10

Length

Max length19
Median length10
Mean length10.602685
Min length10

Characters and Unicode

Total characters7,899
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Somewhat
2nd row3.Somewhat
3rd row3.Somewhat
4th row3.Somewhat
5th row3.Somewhat

Common Values

ValueCountFrequency (%)
3.Somewhat 654
73.2%
4.More than average 35
 
3.9%
2.Very little 34
 
3.8%
5.Very much 12
 
1.3%
1.Not at all 10
 
1.1%
(Missing) 149
 
16.7%

Length

2025-05-15T17:50:02.746312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:02.831527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.somewhat 654
74.2%
4.more 35
 
4.0%
than 35
 
4.0%
average 35
 
4.0%
2.very 34
 
3.9%
little 34
 
3.9%
5.very 12
 
1.4%
much 12
 
1.4%
1.not 10
 
1.1%
at 10
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 839
10.6%
a 779
9.9%
t 777
9.8%
. 745
9.4%
h 701
8.9%
o 699
8.8%
m 666
8.4%
S 654
8.3%
3 654
8.3%
w 654
8.3%
Other values (17) 731
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7899
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 839
10.6%
a 779
9.9%
t 777
9.8%
. 745
9.4%
h 701
8.9%
o 699
8.8%
m 666
8.4%
S 654
8.3%
3 654
8.3%
w 654
8.3%
Other values (17) 731
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7899
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 839
10.6%
a 779
9.9%
t 777
9.8%
. 745
9.4%
h 701
8.9%
o 699
8.8%
m 666
8.4%
S 654
8.3%
3 654
8.3%
w 654
8.3%
Other values (17) 731
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7899
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 839
10.6%
a 779
9.9%
t 777
9.8%
. 745
9.4%
h 701
8.9%
o 699
8.8%
m 666
8.4%
S 654
8.3%
3 654
8.3%
w 654
8.3%
Other values (17) 731
9.3%

knowledge_persecuted_jews
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.7%
Missing150
Missing (%)16.8%
Memory size58.7 KiB
3.Somewhat
640 
4.More than average
 
63
5.Very much
 
34
2.Very little
 
5
1.Not at all
 
2

Length

Max length19
Median length10
Mean length10.833333
Min length10

Characters and Unicode

Total characters8,060
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Somewhat
2nd row3.Somewhat
3rd row3.Somewhat
4th row3.Somewhat
5th row3.Somewhat

Common Values

ValueCountFrequency (%)
3.Somewhat 640
71.6%
4.More than average 63
 
7.0%
5.Very much 34
 
3.8%
2.Very little 5
 
0.6%
1.Not at all 2
 
0.2%
(Missing) 150
 
16.8%

Length

2025-05-15T17:50:03.046531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:03.219750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.somewhat 640
70.1%
4.more 63
 
6.9%
than 63
 
6.9%
average 63
 
6.9%
5.very 34
 
3.7%
much 34
 
3.7%
2.very 5
 
0.5%
little 5
 
0.5%
1.not 2
 
0.2%
at 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 873
10.8%
a 833
10.3%
. 744
9.2%
h 737
9.1%
t 717
8.9%
o 705
8.7%
m 674
8.4%
S 640
7.9%
3 640
7.9%
w 640
7.9%
Other values (17) 857
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 873
10.8%
a 833
10.3%
. 744
9.2%
h 737
9.1%
t 717
8.9%
o 705
8.7%
m 674
8.4%
S 640
7.9%
3 640
7.9%
w 640
7.9%
Other values (17) 857
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 873
10.8%
a 833
10.3%
. 744
9.2%
h 737
9.1%
t 717
8.9%
o 705
8.7%
m 674
8.4%
S 640
7.9%
3 640
7.9%
w 640
7.9%
Other values (17) 857
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 873
10.8%
a 833
10.3%
. 744
9.2%
h 737
9.1%
t 717
8.9%
o 705
8.7%
m 674
8.4%
S 640
7.9%
3 640
7.9%
w 640
7.9%
Other values (17) 857
10.6%

knowledge_other_persecuted_groups
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.7%
Missing150
Missing (%)16.8%
Memory size58.6 KiB
3.Somewhat
652 
4.More than average
 
56
5.Very much
 
23
2.Very little
 
10
1.Not at all
 
3

Length

Max length19
Median length10
Mean length10.75672
Min length10

Characters and Unicode

Total characters8,003
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Somewhat
2nd row3.Somewhat
3rd row3.Somewhat
4th row3.Somewhat
5th row3.Somewhat

Common Values

ValueCountFrequency (%)
3.Somewhat 652
72.9%
4.More than average 56
 
6.3%
5.Very much 23
 
2.6%
2.Very little 10
 
1.1%
1.Not at all 3
 
0.3%
(Missing) 150
 
16.8%

Length

2025-05-15T17:50:03.372256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:03.480220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.somewhat 652
72.8%
4.more 56
 
6.3%
than 56
 
6.3%
average 56
 
6.3%
5.very 23
 
2.6%
much 23
 
2.6%
2.very 10
 
1.1%
little 10
 
1.1%
1.not 3
 
0.3%
at 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 863
10.8%
a 826
10.3%
. 744
9.3%
t 734
9.2%
h 731
9.1%
o 711
8.9%
m 675
8.4%
S 652
8.1%
3 652
8.1%
w 652
8.1%
Other values (17) 763
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 863
10.8%
a 826
10.3%
. 744
9.3%
t 734
9.2%
h 731
9.1%
o 711
8.9%
m 675
8.4%
S 652
8.1%
3 652
8.1%
w 652
8.1%
Other values (17) 763
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 863
10.8%
a 826
10.3%
. 744
9.3%
t 734
9.2%
h 731
9.1%
o 711
8.9%
m 675
8.4%
S 652
8.1%
3 652
8.1%
w 652
8.1%
Other values (17) 763
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 863
10.8%
a 826
10.3%
. 744
9.3%
t 734
9.2%
h 731
9.1%
o 711
8.9%
m 675
8.4%
S 652
8.1%
3 652
8.1%
w 652
8.1%
Other values (17) 763
9.5%
Distinct49
Distinct (%)73.1%
Missing827
Missing (%)92.5%
Memory size38.5 KiB
2025-05-15T17:50:03.837059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length150
Median length71
Mean length37
Min length2

Characters and Unicode

Total characters2,479
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)64.2%

Sample

1st rowJews
2nd rowSinti and Roma__Jews__Political opponents
3rd rowSinti and Roma__Jews__Homosexuals__Disabled people
4th rowOther
5th rowSS
ValueCountFrequency (%)
and 31
 
16.3%
people 14
 
7.4%
sinti 13
 
6.8%
ss 10
 
5.3%
opponents 8
 
4.2%
roma__disabled 7
 
3.7%
roma 6
 
3.2%
jews 4
 
2.1%
jews__homosexuals__sinti 4
 
2.1%
spam 3
 
1.6%
Other values (72) 90
47.4%
2025-05-15T17:50:04.375579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 280
 
11.3%
s 221
 
8.9%
e 208
 
8.4%
o 198
 
8.0%
a 168
 
6.8%
i 157
 
6.3%
n 131
 
5.3%
l 130
 
5.2%
124
 
5.0%
t 96
 
3.9%
Other values (39) 766
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2479
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 280
 
11.3%
s 221
 
8.9%
e 208
 
8.4%
o 198
 
8.0%
a 168
 
6.8%
i 157
 
6.3%
n 131
 
5.3%
l 130
 
5.2%
124
 
5.0%
t 96
 
3.9%
Other values (39) 766
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2479
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 280
 
11.3%
s 221
 
8.9%
e 208
 
8.4%
o 198
 
8.0%
a 168
 
6.8%
i 157
 
6.3%
n 131
 
5.3%
l 130
 
5.2%
124
 
5.0%
t 96
 
3.9%
Other values (39) 766
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2479
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 280
 
11.3%
s 221
 
8.9%
e 208
 
8.4%
o 198
 
8.0%
a 168
 
6.8%
i 157
 
6.3%
n 131
 
5.3%
l 130
 
5.2%
124
 
5.0%
t 96
 
3.9%
Other values (39) 766
30.9%

technologies_used
Categorical

Missing 

Distinct6
Distinct (%)7.7%
Missing816
Missing (%)91.3%
Memory size57.6 KiB
3D model viewer
26 
Virtual Reality (VR)
18 
Augmented Reality (AR)__Virtual Reality (VR)__3D model viewer
15 
Virtual Reality (VR)__3D model viewer
10 
Augmented Reality (AR)

Length

Max length61
Median length44
Mean length29.192308
Min length15

Characters and Unicode

Total characters2,277
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVirtual Reality (VR)
2nd row3D model viewer
3rd row3D model viewer
4th rowVirtual Reality (VR)
5th rowVirtual Reality (VR)__3D model viewer

Common Values

ValueCountFrequency (%)
3D model viewer 26
 
2.9%
Virtual Reality (VR) 18
 
2.0%
Augmented Reality (AR)__Virtual Reality (VR)__3D model viewer 15
 
1.7%
Virtual Reality (VR)__3D model viewer 10
 
1.1%
Augmented Reality (AR) 7
 
0.8%
Augmented Reality (AR)__Virtual Reality (VR) 2
 
0.2%
(Missing) 816
91.3%

Length

2025-05-15T17:50:04.499911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:04.610389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
reality 69
21.7%
model 51
16.0%
viewer 51
16.0%
virtual 28
8.8%
3d 26
 
8.2%
vr)__3d 25
 
7.9%
augmented 24
 
7.5%
vr 20
 
6.3%
ar)__virtual 17
 
5.3%
ar 7
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e 270
 
11.9%
240
 
10.5%
l 165
 
7.2%
i 165
 
7.2%
t 138
 
6.1%
R 138
 
6.1%
a 114
 
5.0%
r 96
 
4.2%
V 90
 
4.0%
_ 84
 
3.7%
Other values (14) 777
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 270
 
11.9%
240
 
10.5%
l 165
 
7.2%
i 165
 
7.2%
t 138
 
6.1%
R 138
 
6.1%
a 114
 
5.0%
r 96
 
4.2%
V 90
 
4.0%
_ 84
 
3.7%
Other values (14) 777
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 270
 
11.9%
240
 
10.5%
l 165
 
7.2%
i 165
 
7.2%
t 138
 
6.1%
R 138
 
6.1%
a 114
 
5.0%
r 96
 
4.2%
V 90
 
4.0%
_ 84
 
3.7%
Other values (14) 777
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 270
 
11.9%
240
 
10.5%
l 165
 
7.2%
i 165
 
7.2%
t 138
 
6.1%
R 138
 
6.1%
a 114
 
5.0%
r 96
 
4.2%
V 90
 
4.0%
_ 84
 
3.7%
Other values (14) 777
34.1%

videogame_frequency
Categorical

Imbalance 

Distinct21
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size55.4 KiB
Invalid
801 
0
 
34
1
 
10
10
 
8
2
 
7
Other values (16)
 
34

Length

Max length7
Median length7
Mean length6.4127517
Min length1

Characters and Unicode

Total characters5,733
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.9%

Sample

1st rowInvalid
2nd rowInvalid
3rd rowInvalid
4th rowInvalid
5th rowInvalid

Common Values

ValueCountFrequency (%)
Invalid 801
89.6%
0 34
 
3.8%
1 10
 
1.1%
10 8
 
0.9%
2 7
 
0.8%
3 5
 
0.6%
20 5
 
0.6%
5 4
 
0.4%
4 3
 
0.3%
8 3
 
0.3%
Other values (11) 14
 
1.6%

Length

2025-05-15T17:50:04.786421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
invalid 801
89.6%
0 34
 
3.8%
1 10
 
1.1%
10 8
 
0.9%
2 7
 
0.8%
3 5
 
0.6%
20 5
 
0.6%
5 4
 
0.4%
4 3
 
0.3%
8 3
 
0.3%
Other values (11) 14
 
1.6%

Most occurring characters

ValueCountFrequency (%)
a 803
14.0%
I 801
14.0%
n 801
14.0%
v 801
14.0%
l 801
14.0%
i 801
14.0%
d 801
14.0%
0 52
 
0.9%
1 21
 
0.4%
2 15
 
0.3%
Other values (11) 36
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 803
14.0%
I 801
14.0%
n 801
14.0%
v 801
14.0%
l 801
14.0%
i 801
14.0%
d 801
14.0%
0 52
 
0.9%
1 21
 
0.4%
2 15
 
0.3%
Other values (11) 36
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 803
14.0%
I 801
14.0%
n 801
14.0%
v 801
14.0%
l 801
14.0%
i 801
14.0%
d 801
14.0%
0 52
 
0.9%
1 21
 
0.4%
2 15
 
0.3%
Other values (11) 36
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 803
14.0%
I 801
14.0%
n 801
14.0%
v 801
14.0%
l 801
14.0%
i 801
14.0%
d 801
14.0%
0 52
 
0.9%
1 21
 
0.4%
2 15
 
0.3%
Other values (11) 36
 
0.6%

felt_part_of_activity
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.6%
Missing172
Missing (%)19.2%
Memory size58.7 KiB
3.Undecided
713 
1.Strongly disagree
 
3
4.Agree
 
3
2.Disagree
 
3

Length

Max length19
Median length11
Mean length11.012465
Min length7

Characters and Unicode

Total characters7,951
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 713
79.8%
1.Strongly disagree 3
 
0.3%
4.Agree 3
 
0.3%
2.Disagree 3
 
0.3%
(Missing) 172
 
19.2%

Length

2025-05-15T17:50:04.923598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:05.011057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 713
98.3%
1.strongly 3
 
0.4%
disagree 3
 
0.4%
4.agree 3
 
0.4%
2.disagree 3
 
0.4%

Most occurring characters

ValueCountFrequency (%)
d 2142
26.9%
e 1444
18.2%
. 722
 
9.1%
i 719
 
9.0%
n 716
 
9.0%
U 713
 
9.0%
3 713
 
9.0%
c 713
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (13) 45
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7951
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2142
26.9%
e 1444
18.2%
. 722
 
9.1%
i 719
 
9.0%
n 716
 
9.0%
U 713
 
9.0%
3 713
 
9.0%
c 713
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (13) 45
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7951
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2142
26.9%
e 1444
18.2%
. 722
 
9.1%
i 719
 
9.0%
n 716
 
9.0%
U 713
 
9.0%
3 713
 
9.0%
c 713
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (13) 45
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7951
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2142
26.9%
e 1444
18.2%
. 722
 
9.1%
i 719
 
9.0%
n 716
 
9.0%
U 713
 
9.0%
3 713
 
9.0%
c 713
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (13) 45
 
0.6%

involvement_over_irrelevant_thoughts
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.6%
Missing173
Missing (%)19.4%
Memory size58.7 KiB
3.Undecided
714 
4.Agree
 
4
2.Disagree
 
2
1.Strongly disagree
 
1

Length

Max length19
Median length11
Mean length10.98613
Min length7

Characters and Unicode

Total characters7,921
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 714
79.9%
4.Agree 4
 
0.4%
2.Disagree 2
 
0.2%
1.Strongly disagree 1
 
0.1%
(Missing) 173
 
19.4%

Length

2025-05-15T17:50:05.159336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:05.292917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 714
98.9%
4.agree 4
 
0.6%
2.disagree 2
 
0.3%
1.strongly 1
 
0.1%
disagree 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 2143
27.1%
e 1442
18.2%
. 721
 
9.1%
i 717
 
9.1%
n 715
 
9.0%
U 714
 
9.0%
3 714
 
9.0%
c 714
 
9.0%
g 8
 
0.1%
r 8
 
0.1%
Other values (13) 25
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2143
27.1%
e 1442
18.2%
. 721
 
9.1%
i 717
 
9.1%
n 715
 
9.0%
U 714
 
9.0%
3 714
 
9.0%
c 714
 
9.0%
g 8
 
0.1%
r 8
 
0.1%
Other values (13) 25
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2143
27.1%
e 1442
18.2%
. 721
 
9.1%
i 717
 
9.1%
n 715
 
9.0%
U 714
 
9.0%
3 714
 
9.0%
c 714
 
9.0%
g 8
 
0.1%
r 8
 
0.1%
Other values (13) 25
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2143
27.1%
e 1442
18.2%
. 721
 
9.1%
i 717
 
9.1%
n 715
 
9.0%
U 714
 
9.0%
3 714
 
9.0%
c 714
 
9.0%
g 8
 
0.1%
r 8
 
0.1%
Other values (13) 25
 
0.3%

experienced_activity_feeling
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.6%
Missing172
Missing (%)19.2%
Memory size58.7 KiB
3.Undecided
714 
2.Disagree
 
4
1.Strongly disagree
 
3
5.Strongly agree
 
1

Length

Max length19
Median length11
Mean length11.034626
Min length10

Characters and Unicode

Total characters7,967
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 714
79.9%
2.Disagree 4
 
0.4%
1.Strongly disagree 3
 
0.3%
5.Strongly agree 1
 
0.1%
(Missing) 172
 
19.2%

Length

2025-05-15T17:50:05.463195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:05.579901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 714
98.3%
2.disagree 4
 
0.6%
1.strongly 3
 
0.4%
disagree 3
 
0.4%
5.strongly 1
 
0.1%
agree 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 2145
26.9%
e 1444
18.1%
. 722
 
9.1%
i 721
 
9.0%
n 718
 
9.0%
3 714
 
9.0%
U 714
 
9.0%
c 714
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (12) 51
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7967
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2145
26.9%
e 1444
18.1%
. 722
 
9.1%
i 721
 
9.0%
n 718
 
9.0%
3 714
 
9.0%
U 714
 
9.0%
c 714
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (12) 51
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7967
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2145
26.9%
e 1444
18.1%
. 722
 
9.1%
i 721
 
9.0%
n 718
 
9.0%
3 714
 
9.0%
U 714
 
9.0%
c 714
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (12) 51
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7967
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2145
26.9%
e 1444
18.1%
. 722
 
9.1%
i 721
 
9.0%
n 718
 
9.0%
3 714
 
9.0%
U 714
 
9.0%
c 714
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (12) 51
 
0.6%

lost_track_of_time
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.7%
Missing172
Missing (%)19.2%
Memory size58.7 KiB
3.Undecided
717 
2.Disagree
 
2
1.Strongly disagree
 
1
4.Agree
 
1
5.Strongly agree
 
1

Length

Max length19
Median length11
Mean length11.009695
Min length7

Characters and Unicode

Total characters7,949
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 717
80.2%
2.Disagree 2
 
0.2%
1.Strongly disagree 1
 
0.1%
4.Agree 1
 
0.1%
5.Strongly agree 1
 
0.1%
(Missing) 172
 
19.2%

Length

2025-05-15T17:50:05.701415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:05.793927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 717
99.0%
2.disagree 2
 
0.3%
1.strongly 1
 
0.1%
disagree 1
 
0.1%
4.agree 1
 
0.1%
5.strongly 1
 
0.1%
agree 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 2152
27.1%
e 1444
18.2%
. 722
 
9.1%
i 720
 
9.1%
n 719
 
9.0%
3 717
 
9.0%
U 717
 
9.0%
c 717
 
9.0%
r 7
 
0.1%
g 7
 
0.1%
Other values (14) 27
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2152
27.1%
e 1444
18.2%
. 722
 
9.1%
i 720
 
9.1%
n 719
 
9.0%
3 717
 
9.0%
U 717
 
9.0%
c 717
 
9.0%
r 7
 
0.1%
g 7
 
0.1%
Other values (14) 27
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2152
27.1%
e 1444
18.2%
. 722
 
9.1%
i 720
 
9.1%
n 719
 
9.0%
3 717
 
9.0%
U 717
 
9.0%
c 717
 
9.0%
r 7
 
0.1%
g 7
 
0.1%
Other values (14) 27
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2152
27.1%
e 1444
18.2%
. 722
 
9.1%
i 720
 
9.1%
n 719
 
9.0%
3 717
 
9.0%
U 717
 
9.0%
c 717
 
9.0%
r 7
 
0.1%
g 7
 
0.1%
Other values (14) 27
 
0.3%

was_interesting
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.6%
Missing174
Missing (%)19.5%
Memory size58.7 KiB
3.Undecided
713 
5.Strongly agree
 
3
4.Agree
 
3
5.Totally agree
 
1

Length

Max length16
Median length11
Mean length11.009722
Min length7

Characters and Unicode

Total characters7,927
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 713
79.8%
5.Strongly agree 3
 
0.3%
4.Agree 3
 
0.3%
5.Totally agree 1
 
0.1%
(Missing) 174
 
19.5%

Length

2025-05-15T17:50:05.920452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:06.005054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 713
98.5%
agree 4
 
0.6%
5.strongly 3
 
0.4%
4.agree 3
 
0.4%
5.totally 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 2139
27.0%
e 1440
18.2%
. 720
 
9.1%
n 716
 
9.0%
3 713
 
9.0%
U 713
 
9.0%
c 713
 
9.0%
i 713
 
9.0%
r 10
 
0.1%
g 10
 
0.1%
Other values (11) 40
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2139
27.0%
e 1440
18.2%
. 720
 
9.1%
n 716
 
9.0%
3 713
 
9.0%
U 713
 
9.0%
c 713
 
9.0%
i 713
 
9.0%
r 10
 
0.1%
g 10
 
0.1%
Other values (11) 40
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2139
27.0%
e 1440
18.2%
. 720
 
9.1%
n 716
 
9.0%
3 713
 
9.0%
U 713
 
9.0%
c 713
 
9.0%
i 713
 
9.0%
r 10
 
0.1%
g 10
 
0.1%
Other values (11) 40
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2139
27.0%
e 1440
18.2%
. 720
 
9.1%
n 716
 
9.0%
3 713
 
9.0%
U 713
 
9.0%
c 713
 
9.0%
i 713
 
9.0%
r 10
 
0.1%
g 10
 
0.1%
Other values (11) 40
 
0.5%

left_weak_impression
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.6%
Missing174
Missing (%)19.5%
Memory size58.7 KiB
3.Undecided
715 
2.Disagree
 
3
5.Strongly agree
 
1
1.Strongly disagree
 
1

Length

Max length19
Median length11
Mean length11.013889
Min length10

Characters and Unicode

Total characters7,930
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 715
80.0%
2.Disagree 3
 
0.3%
5.Strongly agree 1
 
0.1%
1.Strongly disagree 1
 
0.1%
(Missing) 174
 
19.5%

Length

2025-05-15T17:50:06.113316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:06.196941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 715
99.0%
2.disagree 3
 
0.4%
5.strongly 1
 
0.1%
agree 1
 
0.1%
1.strongly 1
 
0.1%
disagree 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 2146
27.1%
e 1440
18.2%
. 720
 
9.1%
i 719
 
9.1%
n 717
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 7
 
0.1%
r 7
 
0.1%
Other values (12) 29
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2146
27.1%
e 1440
18.2%
. 720
 
9.1%
i 719
 
9.1%
n 717
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 7
 
0.1%
r 7
 
0.1%
Other values (12) 29
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2146
27.1%
e 1440
18.2%
. 720
 
9.1%
i 719
 
9.1%
n 717
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 7
 
0.1%
r 7
 
0.1%
Other values (12) 29
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2146
27.1%
e 1440
18.2%
. 720
 
9.1%
i 719
 
9.1%
n 717
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 7
 
0.1%
r 7
 
0.1%
Other values (12) 29
 
0.4%

was_boring
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.6%
Missing174
Missing (%)19.5%
Memory size58.7 KiB
3.Undecided
711 
1.Strongly disagree
 
5
2.Disagree
 
3
5.Strongly agree
 
1

Length

Max length19
Median length11
Mean length11.058333
Min length10

Characters and Unicode

Total characters7,962
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 711
79.5%
1.Strongly disagree 5
 
0.6%
2.Disagree 3
 
0.3%
5.Strongly agree 1
 
0.1%
(Missing) 174
 
19.5%

Length

2025-05-15T17:50:06.338121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:06.425231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 711
97.9%
1.strongly 5
 
0.7%
disagree 5
 
0.7%
2.disagree 3
 
0.4%
5.strongly 1
 
0.1%
agree 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 2138
26.9%
e 1440
18.1%
. 720
 
9.0%
i 719
 
9.0%
n 717
 
9.0%
3 711
 
8.9%
U 711
 
8.9%
c 711
 
8.9%
g 15
 
0.2%
r 15
 
0.2%
Other values (12) 65
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2138
26.9%
e 1440
18.1%
. 720
 
9.0%
i 719
 
9.0%
n 717
 
9.0%
3 711
 
8.9%
U 711
 
8.9%
c 711
 
8.9%
g 15
 
0.2%
r 15
 
0.2%
Other values (12) 65
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2138
26.9%
e 1440
18.1%
. 720
 
9.0%
i 719
 
9.0%
n 717
 
9.0%
3 711
 
8.9%
U 711
 
8.9%
c 711
 
8.9%
g 15
 
0.2%
r 15
 
0.2%
Other values (12) 65
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2138
26.9%
e 1440
18.1%
. 720
 
9.0%
i 719
 
9.0%
n 717
 
9.0%
3 711
 
8.9%
U 711
 
8.9%
c 711
 
8.9%
g 15
 
0.2%
r 15
 
0.2%
Other values (12) 65
 
0.8%

thought_innovative
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.6%
Missing174
Missing (%)19.5%
Memory size58.7 KiB
3.Undecided
715 
5.Strongly agree
 
3
4.Agree
 
1
1.Strongly disagree
 
1

Length

Max length19
Median length11
Mean length11.026389
Min length7

Characters and Unicode

Total characters7,939
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 715
80.0%
5.Strongly agree 3
 
0.3%
4.Agree 1
 
0.1%
1.Strongly disagree 1
 
0.1%
(Missing) 174
 
19.5%

Length

2025-05-15T17:50:06.542274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:06.628391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 715
98.8%
5.strongly 3
 
0.4%
agree 3
 
0.4%
4.agree 1
 
0.1%
1.strongly 1
 
0.1%
disagree 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 2146
27.0%
e 1440
18.1%
. 720
 
9.1%
n 719
 
9.1%
i 716
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 9
 
0.1%
r 9
 
0.1%
Other values (12) 35
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2146
27.0%
e 1440
18.1%
. 720
 
9.1%
n 719
 
9.1%
i 716
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 9
 
0.1%
r 9
 
0.1%
Other values (12) 35
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2146
27.0%
e 1440
18.1%
. 720
 
9.1%
n 719
 
9.1%
i 716
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 9
 
0.1%
r 9
 
0.1%
Other values (12) 35
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2146
27.0%
e 1440
18.1%
. 720
 
9.1%
n 719
 
9.1%
i 716
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 9
 
0.1%
r 9
 
0.1%
Other values (12) 35
 
0.4%

understood_camp_appearance
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.7%
Missing173
Missing (%)19.4%
Memory size58.7 KiB
3.Undecided
711 
4.Agree
 
4
2.Disagree
 
2
5.Strongly agree
 
2
5.Totally agree
 
2

Length

Max length16
Median length11
Mean length11
Min length7

Characters and Unicode

Total characters7,931
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 711
79.5%
4.Agree 4
 
0.4%
2.Disagree 2
 
0.2%
5.Strongly agree 2
 
0.2%
5.Totally agree 2
 
0.2%
(Missing) 173
 
19.4%

Length

2025-05-15T17:50:06.782037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:06.888361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 711
98.1%
4.agree 4
 
0.6%
agree 4
 
0.6%
2.disagree 2
 
0.3%
5.strongly 2
 
0.3%
5.totally 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
n 713
 
9.0%
i 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 52
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
n 713
 
9.0%
i 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 52
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
n 713
 
9.0%
i 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 52
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
n 713
 
9.0%
i 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 52
 
0.7%

understood_life_in_camp
Categorical

High correlation  Imbalance  Missing 

Distinct6
Distinct (%)0.8%
Missing173
Missing (%)19.4%
Memory size58.7 KiB
3.Undecided
712 
2.Disagree
 
2
5.Strongly agree
 
2
4.Agree
 
2
5.Totally agree
 
2

Length

Max length19
Median length11
Mean length11.022191
Min length7

Characters and Unicode

Total characters7,947
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 712
79.6%
2.Disagree 2
 
0.2%
5.Strongly agree 2
 
0.2%
4.Agree 2
 
0.2%
5.Totally agree 2
 
0.2%
1.Strongly disagree 1
 
0.1%
(Missing) 173
 
19.4%

Length

2025-05-15T17:50:07.008004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:07.106684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 712
98.1%
agree 4
 
0.6%
2.disagree 2
 
0.3%
5.strongly 2
 
0.3%
4.agree 2
 
0.3%
5.totally 2
 
0.3%
1.strongly 1
 
0.1%
disagree 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 2137
26.9%
e 1442
18.1%
. 721
 
9.1%
i 715
 
9.0%
n 715
 
9.0%
3 712
 
9.0%
U 712
 
9.0%
c 712
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (15) 57
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7947
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2137
26.9%
e 1442
18.1%
. 721
 
9.1%
i 715
 
9.0%
n 715
 
9.0%
3 712
 
9.0%
U 712
 
9.0%
c 712
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (15) 57
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7947
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2137
26.9%
e 1442
18.1%
. 721
 
9.1%
i 715
 
9.0%
n 715
 
9.0%
3 712
 
9.0%
U 712
 
9.0%
c 712
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (15) 57
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7947
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2137
26.9%
e 1442
18.1%
. 721
 
9.1%
i 715
 
9.0%
n 715
 
9.0%
3 712
 
9.0%
U 712
 
9.0%
c 712
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (15) 57
 
0.7%

understood_camp_function
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.7%
Missing173
Missing (%)19.4%
Memory size58.7 KiB
3.Undecided
711 
2.Disagree
 
3
4.Agree
 
3
5.Strongly agree
 
2
5.Totally agree
 
2

Length

Max length16
Median length11
Mean length11.004161
Min length7

Characters and Unicode

Total characters7,934
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 711
79.5%
2.Disagree 3
 
0.3%
4.Agree 3
 
0.3%
5.Strongly agree 2
 
0.2%
5.Totally agree 2
 
0.2%
(Missing) 173
 
19.4%

Length

2025-05-15T17:50:07.259872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:07.354854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 711
98.1%
agree 4
 
0.6%
2.disagree 3
 
0.4%
4.agree 3
 
0.4%
5.strongly 2
 
0.3%
5.totally 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
i 714
 
9.0%
n 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 54
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
i 714
 
9.0%
n 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 54
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
i 714
 
9.0%
n 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 54
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
i 714
 
9.0%
n 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 54
 
0.7%

felt_sympathetic_to_victims
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing175
Missing (%)19.6%
Memory size58.7 KiB
3.Undecided
719 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters7,909
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 719
80.4%
(Missing) 175
 
19.6%

Length

2025-05-15T17:50:07.473460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:07.541023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 719
100.0%

Most occurring characters

ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

impact_on_own_life
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing175
Missing (%)19.6%
Memory size58.7 KiB
3.Undecided
719 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters7,909
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 719
80.4%
(Missing) 175
 
19.6%

Length

2025-05-15T17:50:07.683684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:07.764407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 719
100.0%

Most occurring characters

ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

impact_on_society
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing175
Missing (%)19.6%
Memory size58.7 KiB
3.Undecided
719 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters7,909
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 719
80.4%
(Missing) 175
 
19.6%

Length

2025-05-15T17:50:07.870486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:07.945935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 719
100.0%

Most occurring characters

ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

impact_society_details
Unsupported

Missing  Rejected  Unsupported 

Missing894
Missing (%)100.0%
Memory size14.0 KiB

want_to_share_learning
Categorical

High correlation  Imbalance  Missing 

Distinct6
Distinct (%)0.8%
Missing169
Missing (%)18.9%
Memory size58.7 KiB
3.Undecided
702 
4.Agree
 
11
5.Strongly agree
 
8
5.Totally agree
 
2
1.Strongly disagree
 
1

Length

Max length19
Median length11
Mean length11.015172
Min length7

Characters and Unicode

Total characters7,986
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 702
78.5%
4.Agree 11
 
1.2%
5.Strongly agree 8
 
0.9%
5.Totally agree 2
 
0.2%
1.Strongly disagree 1
 
0.1%
2.Disagree 1
 
0.1%
(Missing) 169
 
18.9%

Length

2025-05-15T17:50:08.035584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:08.148102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 702
95.4%
4.agree 11
 
1.5%
agree 10
 
1.4%
5.strongly 8
 
1.1%
5.totally 2
 
0.3%
1.strongly 1
 
0.1%
disagree 1
 
0.1%
2.disagree 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 2107
26.4%
e 1450
18.2%
. 725
 
9.1%
n 711
 
8.9%
i 704
 
8.8%
3 702
 
8.8%
U 702
 
8.8%
c 702
 
8.8%
g 32
 
0.4%
r 32
 
0.4%
Other values (15) 119
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2107
26.4%
e 1450
18.2%
. 725
 
9.1%
n 711
 
8.9%
i 704
 
8.8%
3 702
 
8.8%
U 702
 
8.8%
c 702
 
8.8%
g 32
 
0.4%
r 32
 
0.4%
Other values (15) 119
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2107
26.4%
e 1450
18.2%
. 725
 
9.1%
n 711
 
8.9%
i 704
 
8.8%
3 702
 
8.8%
U 702
 
8.8%
c 702
 
8.8%
g 32
 
0.4%
r 32
 
0.4%
Other values (15) 119
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2107
26.4%
e 1450
18.2%
. 725
 
9.1%
n 711
 
8.9%
i 704
 
8.8%
3 702
 
8.8%
U 702
 
8.8%
c 702
 
8.8%
g 32
 
0.4%
r 32
 
0.4%
Other values (15) 119
 
1.5%

plan_to_learn_more
Categorical

High correlation  Imbalance  Missing 

Distinct6
Distinct (%)0.8%
Missing169
Missing (%)18.9%
Memory size58.7 KiB
3.Undecided
705 
4.Agree
 
7
5.Strongly agree
 
7
2.Disagree
 
2
1.Strongly disagree
 
2

Length

Max length19
Median length11
Mean length11.04
Min length7

Characters and Unicode

Total characters8,004
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided

Common Values

ValueCountFrequency (%)
3.Undecided 705
78.9%
4.Agree 7
 
0.8%
5.Strongly agree 7
 
0.8%
2.Disagree 2
 
0.2%
1.Strongly disagree 2
 
0.2%
5.Totally agree 2
 
0.2%
(Missing) 169
 
18.9%

Length

2025-05-15T17:50:08.325559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T17:50:08.451687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.undecided 705
95.8%
agree 9
 
1.2%
4.agree 7
 
1.0%
5.strongly 7
 
1.0%
2.disagree 2
 
0.3%
1.strongly 2
 
0.3%
disagree 2
 
0.3%
5.totally 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
d 2117
26.4%
e 1450
18.1%
. 725
 
9.1%
n 714
 
8.9%
i 709
 
8.9%
3 705
 
8.8%
U 705
 
8.8%
c 705
 
8.8%
g 29
 
0.4%
r 29
 
0.4%
Other values (15) 116
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2117
26.4%
e 1450
18.1%
. 725
 
9.1%
n 714
 
8.9%
i 709
 
8.9%
3 705
 
8.8%
U 705
 
8.8%
c 705
 
8.8%
g 29
 
0.4%
r 29
 
0.4%
Other values (15) 116
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2117
26.4%
e 1450
18.1%
. 725
 
9.1%
n 714
 
8.9%
i 709
 
8.9%
3 705
 
8.8%
U 705
 
8.8%
c 705
 
8.8%
g 29
 
0.4%
r 29
 
0.4%
Other values (15) 116
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2117
26.4%
e 1450
18.1%
. 725
 
9.1%
n 714
 
8.9%
i 709
 
8.9%
3 705
 
8.8%
U 705
 
8.8%
c 705
 
8.8%
g 29
 
0.4%
r 29
 
0.4%
Other values (15) 116
 
1.4%

additional_feedback
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing891
Missing (%)99.7%
Memory size35.0 KiB
2025-05-15T17:50:08.749491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length61
Median length4
Mean length23
Min length4

Characters and Unicode

Total characters69
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowSpam
2nd rowUsability comment: somewhat tricky to use, would retry online
3rd rowdie
ValueCountFrequency (%)
spam 1
9.1%
usability 1
9.1%
comment 1
9.1%
somewhat 1
9.1%
tricky 1
9.1%
to 1
9.1%
use 1
9.1%
would 1
9.1%
retry 1
9.1%
online 1
9.1%
2025-05-15T17:50:09.145098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9
13.0%
t 6
 
8.7%
e 6
 
8.7%
i 5
 
7.2%
o 5
 
7.2%
m 4
 
5.8%
s 3
 
4.3%
a 3
 
4.3%
y 3
 
4.3%
l 3
 
4.3%
Other values (14) 22
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9
13.0%
t 6
 
8.7%
e 6
 
8.7%
i 5
 
7.2%
o 5
 
7.2%
m 4
 
5.8%
s 3
 
4.3%
a 3
 
4.3%
y 3
 
4.3%
l 3
 
4.3%
Other values (14) 22
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9
13.0%
t 6
 
8.7%
e 6
 
8.7%
i 5
 
7.2%
o 5
 
7.2%
m 4
 
5.8%
s 3
 
4.3%
a 3
 
4.3%
y 3
 
4.3%
l 3
 
4.3%
Other values (14) 22
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9
13.0%
t 6
 
8.7%
e 6
 
8.7%
i 5
 
7.2%
o 5
 
7.2%
m 4
 
5.8%
s 3
 
4.3%
a 3
 
4.3%
y 3
 
4.3%
l 3
 
4.3%
Other values (14) 22
31.9%

email
Text

Missing 

Distinct7
Distinct (%)100.0%
Missing887
Missing (%)99.2%
Memory size35.2 KiB
2025-05-15T17:50:09.370261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length21
Mean length19.714286
Min length5

Characters and Unicode

Total characters138
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowparrier@gmail.com
2nd rowbambusapfel@gmail.com
3rd rowkayleemsrielshmar205@gmail.com
4th rowWer@gy-her.de
5th rowmichel.bonkowski@ewe.net
ValueCountFrequency (%)
parrier@gmail.com 1
14.3%
bambusapfel@gmail.com 1
14.3%
kayleemsrielshmar205@gmail.com 1
14.3%
wer@gy-her.de 1
14.3%
michel.bonkowski@ewe.net 1
14.3%
mariazaharia2002@yahoo.co.uk 1
14.3%
roger 1
14.3%
2025-05-15T17:50:09.761846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14
 
10.1%
e 13
 
9.4%
r 11
 
8.0%
m 11
 
8.0%
i 9
 
6.5%
o 9
 
6.5%
. 8
 
5.8%
l 7
 
5.1%
@ 6
 
4.3%
c 5
 
3.6%
Other values (19) 45
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14
 
10.1%
e 13
 
9.4%
r 11
 
8.0%
m 11
 
8.0%
i 9
 
6.5%
o 9
 
6.5%
. 8
 
5.8%
l 7
 
5.1%
@ 6
 
4.3%
c 5
 
3.6%
Other values (19) 45
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14
 
10.1%
e 13
 
9.4%
r 11
 
8.0%
m 11
 
8.0%
i 9
 
6.5%
o 9
 
6.5%
. 8
 
5.8%
l 7
 
5.1%
@ 6
 
4.3%
c 5
 
3.6%
Other values (19) 45
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14
 
10.1%
e 13
 
9.4%
r 11
 
8.0%
m 11
 
8.0%
i 9
 
6.5%
o 9
 
6.5%
. 8
 
5.8%
l 7
 
5.1%
@ 6
 
4.3%
c 5
 
3.6%
Other values (19) 45
32.6%

Correlations

2025-05-15T17:50:09.923983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ageeducation_levelexperienced_activity_feelingfelt_part_of_activitygender_identityinvolvement_over_irrelevant_thoughtsknowledge_bergen_belsenknowledge_other_persecuted_groupsknowledge_persecuted_jewsknowledge_ww2left_weak_impressionlost_track_of_timenationalitypersonal_connection_detailspersonal_connection_nazi_historyplan_to_learn_morepolitical_identityreligioustechnologies_usedthought_innovativeunderstood_camp_appearanceunderstood_camp_functionunderstood_life_in_campvideogame_frequencyvisit_purposevisit_typevisited_memorial_beforewant_to_share_learningwas_boringwas_interesting
age1.0000.4090.0670.0000.3640.0000.1890.0130.0000.1080.1040.0000.1700.2990.2360.3160.1730.2480.2130.0000.3380.3530.3340.1540.0640.4340.2770.2980.1820.000
education_level0.4091.0000.0980.0860.2630.1930.1320.0000.0770.1430.0000.2630.3560.3050.2530.0930.1660.2470.0000.1220.0000.0000.0000.1210.0000.2800.2380.1320.0000.130
experienced_activity_feeling0.0670.0981.0000.6130.0000.7820.2210.1420.2190.1520.0000.8310.0000.0000.0000.4370.2130.0000.0000.0000.0000.0000.0000.3180.0000.0000.1090.4500.0000.000
felt_part_of_activity0.0000.0860.6131.0000.0000.6210.2180.1390.1310.1520.0000.6630.0000.3740.0000.4900.1740.0200.0000.0000.0000.0000.0000.4170.5080.0000.1160.4440.0000.000
gender_identity0.3640.2630.0000.0001.0000.0000.1720.1250.0890.1430.0000.0000.4990.6070.0310.0000.2510.2590.0000.0000.0000.0000.0000.2470.3240.3340.0000.0000.0000.000
involvement_over_irrelevant_thoughts0.0000.1930.7820.6210.0001.0000.2610.1120.1550.1200.0000.7340.0000.0000.0000.5070.2610.0000.0000.0000.0000.0000.0000.2710.0990.0000.0000.6430.0000.000
knowledge_bergen_belsen0.1890.1320.2210.2180.1720.2611.0000.5260.4820.5220.1570.1580.1180.3830.0910.2250.3320.0000.0000.1800.1280.1170.1240.4790.2200.2000.1080.2330.1360.155
knowledge_other_persecuted_groups0.0130.0000.1420.1390.1250.1120.5261.0000.6870.7040.1680.1010.3240.0000.0000.2410.3460.1180.1540.1600.1620.1330.2090.4540.1250.0000.1010.2400.1720.196
knowledge_persecuted_jews0.0000.0770.2190.1310.0890.1550.4820.6871.0000.6530.0760.1480.4760.5110.1310.1890.3110.0000.0000.0460.1940.1780.1480.4390.1790.0150.1420.2310.0740.119
knowledge_ww20.1080.1430.1520.1520.1430.1200.5220.7040.6531.0000.0780.0830.4240.0000.2340.1890.3490.0000.0560.0420.1670.1690.1620.4860.1280.0000.1520.2090.0890.107
left_weak_impression0.1040.0000.0000.0000.0000.0000.1570.1680.0760.0781.0000.0000.0000.0000.0000.4830.1230.0000.3140.5060.0000.0000.0000.2610.0000.0000.0580.4520.8010.426
lost_track_of_time0.0000.2630.8310.6630.0000.7340.1580.1010.1480.0830.0001.0000.0001.0000.0000.5460.1910.0150.0000.0000.0000.0000.0000.4000.0000.0000.0740.5700.0000.000
nationality0.1700.3560.0000.0000.4990.0000.1180.3240.4760.4240.0000.0001.0000.4880.5650.0000.0000.1070.2590.0000.0000.0000.0000.0000.0000.3290.0670.0000.1850.473
personal_connection_details0.2990.3050.0000.3740.6070.0000.3830.0000.5110.0000.0001.0000.4881.0001.0000.0000.0000.2980.0000.0000.0000.0000.0000.2500.2980.3470.0000.0000.0000.000
personal_connection_nazi_history0.2360.2530.0000.0000.0310.0000.0910.0000.1310.2340.0000.0000.5651.0001.0000.1120.0230.0670.1840.0000.1390.1910.2170.1510.0000.1880.0520.0000.0000.140
plan_to_learn_more0.3160.0930.4370.4900.0000.5070.2250.2410.1890.1890.4830.5460.0000.0000.1121.0000.1860.0000.3450.3750.4290.4010.4530.4410.0000.0000.1220.7510.4940.662
political_identity0.1730.1660.2130.1740.2510.2610.3320.3460.3110.3490.1230.1910.0000.0000.0230.1861.0000.1970.0000.1890.1080.1170.1170.3870.2130.1360.1660.1880.1300.168
religious0.2480.2470.0000.0200.2590.0000.0000.1180.0000.0000.0000.0150.1070.2980.0670.0000.1971.0000.0730.0690.0790.1770.1540.0820.2320.2170.0900.0000.0550.000
technologies_used0.2130.0000.0000.0000.0000.0000.0000.1540.0000.0560.3140.0000.2590.0000.1840.3450.0000.0731.0000.1190.1950.1420.2350.1030.0410.0980.1650.3580.1410.382
thought_innovative0.0000.1220.0000.0000.0000.0000.1800.1600.0460.0420.5060.0000.0000.0000.0000.3750.1890.0690.1191.0000.0000.0000.0000.3730.0000.1160.1060.3330.5130.505
understood_camp_appearance0.3380.0000.0000.0000.0000.0000.1280.1620.1940.1670.0000.0000.0000.0000.1390.4290.1080.0790.1950.0001.0000.9230.9000.4770.1070.0000.0700.4200.0000.000
understood_camp_function0.3530.0000.0000.0000.0000.0000.1170.1330.1780.1690.0000.0000.0000.0000.1910.4010.1170.1770.1420.0000.9231.0000.9560.4400.1210.0000.0650.4220.0000.000
understood_life_in_camp0.3340.0000.0000.0000.0000.0000.1240.2090.1480.1620.0000.0000.0000.0000.2170.4530.1170.1540.2350.0000.9000.9561.0000.4870.0000.0000.0000.3800.0000.000
videogame_frequency0.1540.1210.3180.4170.2470.2710.4790.4540.4390.4860.2610.4000.0000.2500.1510.4410.3870.0820.1030.3730.4770.4400.4871.0000.2430.0000.0520.3120.2230.293
visit_purpose0.0640.0000.0000.5080.3240.0990.2200.1250.1790.1280.0000.0000.0000.2980.0000.0000.2130.2320.0410.0000.1070.1210.0000.2431.0000.2020.1420.0000.0000.000
visit_type0.4340.2800.0000.0000.3340.0000.2000.0000.0150.0000.0000.0000.3290.3470.1880.0000.1360.2170.0980.1160.0000.0000.0000.0000.2021.0000.2270.0000.0000.000
visited_memorial_before0.2770.2380.1090.1160.0000.0000.1080.1010.1420.1520.0580.0740.0670.0000.0520.1220.1660.0900.1650.1060.0700.0650.0000.0520.1420.2271.0000.1080.1140.108
want_to_share_learning0.2980.1320.4500.4440.0000.6430.2330.2400.2310.2090.4520.5700.0000.0000.0000.7510.1880.0000.3580.3330.4200.4220.3800.3120.0000.0000.1081.0000.4030.633
was_boring0.1820.0000.0000.0000.0000.0000.1360.1720.0740.0890.8010.0000.1850.0000.0000.4940.1300.0550.1410.5130.0000.0000.0000.2230.0000.0000.1140.4031.0000.458
was_interesting0.0000.1300.0000.0000.0000.0000.1550.1960.1190.1070.4260.0000.4730.0000.1400.6620.1680.0000.3820.5050.0000.0000.0000.2930.0000.0000.1080.6330.4581.000

Missing values

2025-05-15T17:49:57.024988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-15T17:49:57.643279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-15T17:49:58.482962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

nationalitygender_identityageeducation_levelvisit_typevisit_purposereligiouspolitical_identityvisited_memorial_beforepersonal_connection_nazi_historypersonal_connection_detailsknowledge_ww2knowledge_bergen_belsenknowledge_persecuted_jewsknowledge_other_persecuted_groupsknown_persecuted_groups_opentechnologies_usedvideogame_frequencyfelt_part_of_activityinvolvement_over_irrelevant_thoughtsexperienced_activity_feelinglost_track_of_timewas_interestingleft_weak_impressionwas_boringthought_innovativeunderstood_camp_appearanceunderstood_life_in_campunderstood_camp_functionfelt_sympathetic_to_victimsimpact_on_own_lifeimpact_on_societyimpact_society_detailswant_to_share_learningplan_to_learn_moreadditional_feedbackemail
1732835511436NaNNaNNaNNaNNaNNaNNaN3.CentreNaNNaNNaN3.Somewhat3.Somewhat3.Somewhat3.SomewhatNaNNaNInvalid3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.UndecidedNaN3.Undecided3.UndecidedNaNNaN
1732836715570NaNNaNNaNNaNNaNNaNNaN3.CentreNaNNaNNaN3.Somewhat3.Somewhat3.Somewhat3.SomewhatNaNNaNInvalid3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.UndecidedNaN3.Undecided3.UndecidedNaNNaN
1732837590169NaNNaNNaNNaNNaNNaNNaN3.CentreNaNNaNNaN3.Somewhat3.Somewhat3.Somewhat3.SomewhatNaNNaNInvalid3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.UndecidedNaN3.Undecided3.UndecidedNaNNaN
1732838671324NaNNaNNaNNaNNaNNaNNaN3.CentreNaNNaNNaN3.Somewhat3.Somewhat3.Somewhat3.SomewhatNaNNaNInvalid3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.UndecidedNaN3.Undecided3.UndecidedNaNNaN
1732838916442NaNNaNNaNNaNNaNNaNNaN3.CentreNaNNaNNaN3.Somewhat3.Somewhat3.Somewhat3.SomewhatNaNNaNInvalid3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.UndecidedNaN3.Undecided3.UndecidedNaNNaN
1732843110739GermanyNaNNaNNaNNaNNaNNaN3.CentreNaNNaNNaN3.Somewhat3.Somewhat3.Somewhat3.SomewhatNaNNaNInvalid3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.UndecidedNaN3.Undecided3.UndecidedNaNNaN
1732843414817NaNNaNNaNNaNNaNNaNNaN3.CentreNaNNaNNaN3.Somewhat3.Somewhat3.Somewhat3.SomewhatNaNNaNInvalid3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.UndecidedNaN3.Undecided3.UndecidedNaNNaN
1732858522286NaNNaNNaNNaNNaNNaNNaN3.CentreNaNNaNNaN3.Somewhat3.Somewhat3.Somewhat3.SomewhatNaNNaNInvalid3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.UndecidedNaN3.Undecided3.UndecidedNaNNaN
1732871943555NaNNaNNaNNaNNaNNaNNaN3.CentreNaNNaNNaN3.Somewhat3.Somewhat3.Somewhat3.SomewhatNaNNaNInvalid3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.UndecidedNaN3.Undecided3.UndecidedNaNNaN
1732880094401NaNNaNNaNNaNNaNNaNNaN3.CentreNaNNaNNaN3.Somewhat3.Somewhat3.Somewhat3.SomewhatNaNNaNInvalid3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.UndecidedNaN3.Undecided3.UndecidedNaNNaN
nationalitygender_identityageeducation_levelvisit_typevisit_purposereligiouspolitical_identityvisited_memorial_beforepersonal_connection_nazi_historypersonal_connection_detailsknowledge_ww2knowledge_bergen_belsenknowledge_persecuted_jewsknowledge_other_persecuted_groupsknown_persecuted_groups_opentechnologies_usedvideogame_frequencyfelt_part_of_activityinvolvement_over_irrelevant_thoughtsexperienced_activity_feelinglost_track_of_timewas_interestingleft_weak_impressionwas_boringthought_innovativeunderstood_camp_appearanceunderstood_life_in_campunderstood_camp_functionfelt_sympathetic_to_victimsimpact_on_own_lifeimpact_on_societyimpact_society_detailswant_to_share_learningplan_to_learn_moreadditional_feedbackemail
1747128556212GermanyMale65–74Secondary schoolAs an adult with other adultsTo learn more about the historyYes5.Far rightNoNoNaN3.Somewhat1.Not at all4.More than average4.More than averageNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1747128869309NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNInvalidNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1747208473170GermanyFemaleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNInvalidNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1747209305377NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNInvalidNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1747210555161NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNInvalidNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1747211914955NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNInvalidNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1747212433303NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNInvalidNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1747213562795DenmarkFemale65–74High schoolAs an adult with other adultsTo learn more about the historyYes3.CentreNonoNaN4.More than average3.Somewhat5.Very much5.Very muchJews__Communists__Homosexuals3D model viewer0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1747216091129NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNInvalidNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1747216282932GermanyFemale45–54Bachelor's degreeAs an adult with other adultsTo learn more about the historyYes2.Left of centreYes, more than once beforeNoNaN4.More than average3.Somewhat5.Very much5.Very muchSinti and Roma__Jews__Homosexuals__Political opponents__Social DemocratsAugmented Reality (AR)0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

nationalitygender_identityageeducation_levelvisit_typevisit_purposereligiouspolitical_identityvisited_memorial_beforepersonal_connection_nazi_historypersonal_connection_detailsknowledge_ww2knowledge_bergen_belsenknowledge_persecuted_jewsknowledge_other_persecuted_groupsknown_persecuted_groups_opentechnologies_usedvideogame_frequencyfelt_part_of_activityinvolvement_over_irrelevant_thoughtsexperienced_activity_feelinglost_track_of_timewas_interestingleft_weak_impressionwas_boringthought_innovativeunderstood_camp_appearanceunderstood_life_in_campunderstood_camp_functionfelt_sympathetic_to_victimsimpact_on_own_lifeimpact_on_societywant_to_share_learningplan_to_learn_moreadditional_feedbackemail# duplicates
1NaNNaNNaNNaNNaNNaNNaN3.CentreNaNNaNNaN3.Somewhat3.Somewhat3.Somewhat3.SomewhatNaNNaNInvalid3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.UndecidedNaNNaN566
2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNInvalidNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN135
0GermanyNaNNaNNaNNaNNaNNaN3.CentreNaNNaNNaN3.Somewhat3.Somewhat3.Somewhat3.SomewhatNaNNaNInvalid3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.Undecided3.UndecidedNaNNaN4